@inproceedings{dai-lin-2026-alps,
    title = "{ALPS}-Lab at {S}em{E}val-2026 Task 3: A Multilingual Generative {LLM} Approach for Dimensional Aspect Sentiment Analysis",
    author = "Dai, Songqian  and
      Lin, Wei",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.212/",
    pages = "1652--1658",
    ISBN = "979-8-89176-414-9",
    abstract = "We propose a SFT approach for the DimABSA shared task, which predicts aspect-level sentiment intensities using large language models. The approach uses Gemma-3 27B with QLoRA for efficient fine-tuning on multilingual datasets. Merging data across languages improves performance, especially in low-resource domains. Post-processing removes duplicate outputs for accurate evaluation."
}